Global investigation of pedestrian-level cooling and energy-saving potentials of green and cool roofs in 43 megacities
Bibliographic record
Abstract
• A coupled approach combining a microclimatic model and energy simulation is proposed. • Green roofs demonstrate a significant cooling effect at the pedestrian level. • Open layouts are more suitable for implementing roof-level strategies for cooling. • Cool roofs are less effective in saving energy in higher-latitude regions. • Green roofs consistently provide energy savings for both cooling and heating. Green roofs and cool roofs are emerging as two potential solutions to combat the negative impacts of urban warming in the context of climate change. However, the existing body of research has not clearly established the connection between the local built environment and the effectiveness of these solutions. Moreover, a lack of standardized methodologies for integrating micro-scale climatic data has impeded the precision of modeling endeavors. In light of these knowledge gaps, an extensive study was conducted across 43 megacities to evaluate the impact of green and cool roofs on reducing urban temperatures and building energy consumption. A novel integrated approach, combining a micro-level computational fluid dynamics (CFD) model and a building energy simulation method, was used. The results reveal that both cool and green roofs moderately cool pedestrian areas, with green roofs slightly outperforming cool roofs, reducing temperatures by an average of 0.10 °C. Delhi reported the highest cooling effect from green roofs at 0.80 °C, while Beijing recorded the top cooling performance from cool roofs at 0.23 °C. Cool roofs showed significant cooling energy savings, from 5.4 to 63.8 kWh/m 2 /year, particularly in sun-drenched cities like Bangalore, Dhaka, and Ahmedabad, albeit their inability to save heating energy in higher latitudes. Conversely, green roofs provided consistent energy savings, typically from 1.1 to 7.3 kWh/m 2 /year, with Dhaka exhibiting the highest energy-saving amount. Additionally, the study also identified that urban morphology influences the effectiveness of these strategies. The cooling effect becomes less noticeable with increasing building height, and open layouts are more conducive to roof-level strategies. The findings from this study will help optimize the implementation of these strategies in different climates and built environments, contributing to efforts to mitigate global climate change and enhance urban livability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".